Precision nephrology identified tumor necrosis factor activation variability in minimal change disease and focal segmental glomerulosclerosis
Authors: Laura H. Mariani, Sean Eddy, Fadhl M. AlAkwaa, Phillip J. McCown, Jennifer L. Harder, Viji Nair, Felix Eichinger, Sebastian Martini, Adebowale D. Ademola, Vincent Boima, Heather N. Reich, Jamal El Saghir, Bradley Godfrey, Wenjun Ju, Emily C. Tanner, Virginia Vega-Warner, Noel L. Wys, Sharon G. Adler, Gerald B. Appel, Ambarish Athavale, Meredith A. Atkinson, Serena M. Bagnasco, Laura Barisoni, Elizabeth Brown, Daniel C. Cattran, Gaia M. Coppock, Katherine M. Dell, Vimal K. Derebail, Fernando C. Fervenza, Alessia Fornoni, Crystal A. Gadegbeku, Keisha L. Gibson, Laurence A. Greenbaum, Sangeeta R. Hingorani, Michelle A. Hladunewich, Jeffrey B. Hodgin, Marie C. Hogan, Lawrence B. Holzman, J. Ashley Jefferson, Frederick J. Kaskel, Jeffrey B. Kopp, Richard A. Lafayette, Kevin V. Lemley, John C. Lieske, Jen-Jar Lin, Rajarasee Menon, Kevin E. Meyers, Patrick H. Nachman, Cynthia C. Nast, Michelle M. O’Shaughnessy, Edgar A. Otto, Kimberly J. Reidy, Kamalanathan K. Sambandam, John R. Sedor, Christine B. Sethna, Pamela Singer, Tarak Srivastava, Cheryl L. Tran, Katherine R. Tuttle, Suzanne M. Vento, Chia-shi Wang, Akinlolu O. Ojo, Dwomoa Adu, Debbie S. Gipson, Howard Trachtman, Matthias Kretzler
Journal: Kidney International (2022)

The diagnosis of nephrotic syndrome relies on clinical presentation and descriptive patterns of injury on kidney biopsies, but not specific to underlying pathobiology. Consequently, there are variable rates of progression and response to therapy within diagnoses. Here, an unbiased transcriptomic-driven approach was used to identify molecular pathways which are shared by subgroups of patients with either minimal change disease (MCD) or focal segmental glomerulosclerosis (FSGS). Kidney tissue transcriptomic profile-based clustering identified three patient subgroups with shared molecular signatures across independent, North American, European, and African cohorts. One subgroup had significantly greater disease progression (Hazard Ratio 5.2) which persisted after adjusting for diagnosis and clinical measures (Hazard Ratio 3.8). Inclusion in this subgroup was retained even when clustering was limited to those with less than 25% interstitial fibrosis. The molecular profile of this subgroup was largely consistent with tumor necrosis factor (TNF) pathway activation. Two TNF pathway urine markers were identified, tissue inhibitor of metalloproteinases-1 (TIMP-1) and monocyte chemoattractant protein-1 (MCP-1), that could be used to predict an individual's TNF pathway activation score. Kidney organoids and single-nucleus RNA-sequencing of participant kidney biopsies, validated TNF-dependent increases in pathway activation score, transcript and protein levels of TIMP-1 and MCP-1, in resident kidney cells. Thus, molecular profiling identified a subgroup of patients with either MCD or FSGS who shared kidney TNF pathway activation and poor outcomes. A clinical trial testing targeted therapies in patients selected using urinary markers of TNF pathway activation is ongoing. The diagnosis of nephrotic syndrome relies on clinical presentation and descriptive patterns of injury on kidney biopsies, but not specific to underlying pathobiology. Consequently, there are variable rates of progression and response to therapy within diagnoses. Here, an unbiased transcriptomic-driven approach was used to identify molecular pathways which are shared by subgroups of patients with either minimal change disease (MCD) or focal segmental glomerulosclerosis (FSGS). Kidney tissue transcriptomic profile-based clustering identified three patient subgroups with shared molecular signatures across independent, North American, European, and African cohorts. One subgroup had significantly greater disease progression (Hazard Ratio 5.2) which persisted after adjusting for diagnosis and clinical measures (Hazard Ratio 3.8). Inclusion in this subgroup was retained even when clustering was limited to those with less than 25% interstitial fibrosis. The molecular profile of this subgroup was largely consistent with tumor necrosis factor (TNF) pathway activation. Two TNF pathway urine markers were identified, tissue inhibitor of metalloproteinases-1 (TIMP-1) and monocyte chemoattractant protein-1 (MCP-1), that could be used to predict an individual's TNF pathway activation score. Kidney organoids and single-nucleus RNA-sequencing of participant kidney biopsies, validated TNF-dependent increases in pathway activation score, transcript and protein levels of TIMP-1 and MCP-1, in resident kidney cells. Thus, molecular profiling identified a subgroup of patients with either MCD or FSGS who shared kidney TNF pathway activation and poor outcomes. A clinical trial testing targeted therapies in patients selected using urinary markers of TNF pathway activation is ongoing. Nephrotic syndrome is characterized by proteinuria, hypoalbuminemia, hyperlipidemia, and edema. Two nephrotic diseases, minimal change disease (MCD) and focal segmental glomerulosclerosis (FSGS), currently diagnosed based on histopathologic features, have broadly overlapping clinical presentations and treatment approaches.1Kidney Disease: Improving Global Outcomes (KDIGO) Glomerular Diseases Work GroupKDIGO 2021 clinical practice guideline for the management of glomerular diseases.Kidney Int. 2021; 100: S1-S276PubMed Scopus (654) Google Scholar Within each diagnosis, however, some patients respond well to current therapy, whereas others either do not respond or relapse upon stopping therapy with variable risk of loss of kidney function. These clinical observations suggest a heterogeneous biology underlying current disease classification.2D'Agati V.D. Alster J.M. Jennette J.C. et al.Association of histologic variants in FSGS clinical trial with presenting features and outcomes.Clin J Am Soc Nephrol. 2013; 8: 399-406Crossref PubMed Scopus (109) Google Scholar, 3Rosenberg A.Z. 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Nevertheless, nephrotic syndrome is well positioned for implementing precision medicine. Clinically procured kidney biopsy tissue allows for unbiased identification of molecular signatures that can be linked to histopathology, noninvasive biomarkers, and evaluated against clinical outcomes. With the goal of identifying molecular pathways that are shared by subgroups of patients with MCD and FSGS, this study implemented a multidimensional data integration approach (Figure 1) in the prospective North American Nephrotic Syndrome Study Network (NEPTUNE),9Barisoni L. Nast C.C. Jennette J.C. et al.Digital pathology evaluation in the multicenter Nephrotic Syndrome Study Network (NEPTUNE).Clin J Am Soc Nephrol. 2013; 8: 1449-1459Crossref PubMed Scopus (67) Google Scholar then replicated in the European Renal cDNA Bank (ERCB)10Yasuda Y. Cohen C.D. 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Olanrewaju T. et al.Genomic approaches to the burden of kidney disease in Sub-Saharan Africa: the Human Heredity and Health in Africa (H3Africa) Kidney Disease Research Network.Kidney Int. 2016; 90: 2-5Abstract Full Text Full Text PDF PubMed Scopus (22) Google Scholar The study aims to agnostically identify groups of patients with shared molecular signatures, to identify the relevant pathways from that signature that could then be evaluated in individual patients using potential noninvasive markers. Such markers of the disease mechanism may enable targeted therapeutic interventions. The study included 220 NEPTUNE,9Barisoni L. Nast C.C. Jennette J.C. et al.Digital pathology evaluation in the multicenter Nephrotic Syndrome Study Network (NEPTUNE).Clin J Am Soc Nephrol. 2013; 8: 1449-1459Crossref PubMed Scopus (67) Google Scholar 35 H3Africa,12Osafo C. Raji Y.R. Burke D. et al.Human Heredity and Health (H3) in Africa Kidney Disease Research Network: a focus on methods in Sub-Saharan Africa.Clin J Am Soc Nephrol. 2015; 10: 2279-2287Crossref PubMed Scopus (40) Google Scholar and 30 ERCB10Yasuda Y. Cohen C.D. Henger A. et al.Gene expression profiling analysis in nephrology: towards molecular definition of renal disease.Clin Exp Nephrol. 2006; 10: 91-98Crossref PubMed Scopus (61) Google Scholar,11Schmid H. Boucherot A. Yasuda Y. et al.Modular activation of nuclear factor-kappaB transcriptional programs in human diabetic nephropathy.Diabetes. 2006; 55: 2993-3003Crossref PubMed Scopus (350) Google Scholar participants with biopsy-proven MCD or FSGS and compartment-enriched genome-wide kidney mRNA expression profiles. NEPTUNE (NCT01209000) is a prospective study of children and adults with proteinuria, recruited from 21 sites at the time of their first clinically indicated kidney biopsy.9Barisoni L. Nast C.C. Jennette J.C. et al.Digital pathology evaluation in the multicenter Nephrotic Syndrome Study Network (NEPTUNE).Clin J Am Soc Nephrol. 2013; 8: 1449-1459Crossref PubMed Scopus (67) Google Scholar,14Gadegbeku C.A. Gipson D.S. Holzman L.B. et al.Design of the Nephrotic Syndrome Study Network (NEPTUNE) to evaluate primary glomerular nephropathy by a multidisciplinary approach.Kidney Int. 2013; 83: 749-756Abstract Full Text Full Text PDF PubMed Scopus (215) Google Scholar ERCB is a European study of adults recruited at 28 sites, with biopsy tissue for gene expression profiling and cross-sectional clinical information, at the time of a clinically indicated kidney biopsy.10Yasuda Y. Cohen C.D. Henger A. et al.Gene expression profiling analysis in nephrology: towards molecular definition of renal disease.Clin Exp Nephrol. 2006; 10: 91-98Crossref PubMed Scopus (61) Google Scholar,11Schmid H. Boucherot A. Yasuda Y. et al.Modular activation of nuclear factor-kappaB transcriptional programs in human diabetic nephropathy.Diabetes. 2006; 55: 2993-3003Crossref PubMed Scopus (350) Google Scholar H3Africa12Osafo C. Raji Y.R. Burke D. et al.Human Heredity and Health (H3) in Africa Kidney Disease Research Network: a focus on methods in Sub-Saharan Africa.Clin J Am Soc Nephrol. 2015; 10: 2279-2287Crossref PubMed Scopus (40) Google Scholar is a prospective study of participants aged 15 years and above, eligible for a kidney biopsy, recruited from 13 clinical centers in Nigeria and Ghana with an estimated glomerular filtration rate (eGFR) of ≥15 ml/min per 1.73 m2 and proteinuria (albuminuria >500 mg/d).12Osafo C. Raji Y.R. Burke D. et al.Human Heredity and Health (H3) in Africa Kidney Disease Research Network: a focus on methods in Sub-Saharan Africa.Clin J Am Soc Nephrol. 2015; 10: 2279-2287Crossref PubMed Scopus (40) Google Scholar For all cohorts, informed consent was obtained from individual patients or parents/guardians on approval by institutional review boards or local ethics committees of participating institutions. NEPTUNE participants were followed 2 to 3 times per year, for up to 5 years. Medical history, medication use, laboratory results, blood and urine samples for the measurement of serum creatinine, and urine protein-to-creatinine ratio (UPCR) were collected at each study visit. eGFR (ml/min per 1.73 m2) was calculated using the Chronic Kidney Disease Epidemiology Collaboration formula for participants ≥18 years old and the modified chronic kidney disease in children-Schwartz formula for participants <18 years old, with an average taken for young adults aged 18 to 26 years.15Levey A.S. Bosch J.P. Lewis J.B. et al.A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group.Ann Intern Med. 1999; 130: 461-470Crossref PubMed Scopus (13381) Google Scholar, 16Schwartz G.J. Work D.F. Measurement and estimation of GFR in children and adolescents.Clin J Am Soc Nephrol. 2009; 4: 1832-1843Crossref PubMed Scopus (737) Google Scholar, 17Ng D.K. Schwartz G.J. Schneider M.F. et al.Combination of pediatric and adult formulas yield valid glomerular filtration rate estimates in young adults with a history of pediatric chronic kidney disease.Kidney Int. 2018; 94: 170-177Abstract Full Text Full Text PDF PubMed Scopus (58) Google Scholar The endpoint of kidney functional loss was defined by a 40% reduction in eGFR or the onset of kidney failure (initiation of dialysis, receipt of kidney transplant or eGFR <15 ml/min per 1.73 m2 measured at 2 sequential visits).18Zee J. Mansfield S. Mariani L.H. Gillespie B.W. Using all longitudinal data to define time to specified percentages of estimated GFR decline: a simulation study.Am J Kidney Dis. 2019; 73: 82-89Abstract Full Text Full Text PDF PubMed Scopus (14) Google Scholar Complete remission was defined as UPCR <0.3 mg/mg on a single-void urine or a 24-hour urine collection. In ERCB and H3Africa, clinical information, including demographics and clinical laboratory results, was obtained at the time of biopsy. Diagnosis in NEPTUNE was assigned by study pathologists based on biopsy reports and/or digital images. The degree of interstitial fibrosis (IF) was visually assessed and scored by 2 to 5 pathologists using biopsy whole slide images of trichrome, periodic acid–Schiff, or silver-stained sections, recorded as the percent of cortex involved, and averaged across the pathologists' measures.19Grayson P.C. Eddy S. Taroni J.N. et al.Metabolic pathways and immunometabolism in rare kidney diseases.Ann Rheum Dis. 2018; 77: 1226-1233PubMed Google Scholar,20Barisoni L. Troost J.P. Nast C. et al.Reproducibility of the NEPTUNE descriptor-based scoring system on whole-slide images and histologic and ultrastructural digital images.Mod Pathol. 2016; 29: 671-684Abstract Full Text Full Text PDF PubMed Scopus (46) Google Scholar In NEPTUNE, RNA sequencing (RNA-seq) was performed on manually microdissected kidney biopsy tissue that separated tubulointerstitial and glomerular compartments of the research core. For H3Africa, a 5-mm cortical segment (not needed for clinical diagnosis) was manually microdissected, RNA-isolated, and sequenced to generate RNA-seq profiles (see Supplementary Methods). In ERCB, compartment-specific transcriptomic profiles of the research core were generated using the Affymetrix microarray platform. NEPTUNE RNA-seq and ERCB microarray data are available at Nephroseq.org and through Gene Expression Omnibus21Edgar R. Domrachev M. Lash A.E. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository.Nucleic Acids Res. 2002; 30: 207-210Crossref PubMed Scopus (9144) Google Scholar (GSE219185, GSE197307, GSE104954, GSE104948). R Software (R Core Team [2013]) was used for Kmeans, Prediction Analysis of Microarrays (PAM), and hierarchical clustering analyses.22Wilkerson M.D. Hayes D.N. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking.Bioinformatics. 2010; 26: 1572-1573Crossref PubMed Scopus (2603) Google Scholar,23Monti S. Tamayo P. Mesirov J.P. Golub T. Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data.Mach Learn. 2004; 52: 91-118Crossref Scopus (1424) Google Scholar Optimal clustering was determined using delta-K and the ConsensusClusterPlus package.22Wilkerson M.D. Hayes D.N. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking.Bioinformatics. 2010; 26: 1572-1573Crossref PubMed Scopus (2603) Google Scholar The linear models for microarray data (limma) package24Ritchie M.E. Phipson B. Wu D. et al.limma powers differential expression analyses for RNA-sequencing and microarray studies.Nucleic Acids Res. 2015; 43: e47Crossref PubMed Scopus (18703) Google Scholar was used for differential expression analysis. Differentially expressed genes (absolute fold change >1.5 and q value <0.05) between clusters were analyzed for the enrichment of canonical pathways using the Ingenuity Pathway Analysis Software Suite (IPA; Qiagen).25Calvano S.E. Xiao W. Richards D.R. et al.A network-based analysis of systemic inflammation in humans.Nature. 2005; 437: 1032-1037Crossref PubMed Scopus (1244) Google Scholar Previously published26Menon R. Otto E.A. Hoover P. et al.Single cell transcriptomics identifies focal segmental glomerulosclerosis remission endothelial biomarker.JCI Insight. 2020; 5e133267Crossref Scopus (77) Google Scholar single-cell RNA-seq cell-type selective expression clusters from adult reference kidney tissue can be accessed at http://nephrocell.miktmc.org/ and GEO (GSE14098). A tumor necrosis factor (TNF) activation network was generated from expert curated interactions from NETPro annotations in the Genomatix Genome Analyzer database (Precigen Bioinformatics). From the database, 272 causally downstream genes or proteins that increased expression from TNF exposure were used to generate a TNF activation score. Individual gene expression values were first Z-transformed. The TNF activation score for each participant was the average Z-score of the 272 genes in their kidney RNA-seq profile. Urine proteins were identified and measured using the multiplex Luminex platform (Eve Technologies) composed of a panel of 54 urinary cytokines, matrix metalloproteinases, and tissue inhibitor of metalloproteinases (see Supplementary Methods). A candidate protein had to satisfy the following criteria to be considered a potential noninvasive marker of TNF activation: (i) protein must be a product of a gene expressed downstream of TNF; (ii) urine protein expression must correlate with the corresponding kidney tissue gene expression (mRNA levels), and (iii) gene expression must correlate with the TNF activation score. Putative urine markers were assayed in duplicate using Quantikine enzyme-linked immunosorbent assay kit human chemokine (C-C motif) ligand 2 (CCL2)/monocyte chemoattractant protein-1 (MCP-1) (DCP00) and tissue inhibitor of metalloproteinases 1 (TIMP-1) (DTM100; R&D Systems). Absorbance was measured with a VersaMax enzyme-linked immunosorbent assay plate reader, and results were calculated with SoftMax Pro (Molecular Devices). Markers were normalized to urine creatinine concentration and log2 transformed. Descriptive statistics were used to characterize baseline (time of biopsy) participant characteristics by molecular cluster. Differences in Kaplan-Meier curves, by molecular cluster, were tested by the log-rank test. Univariate Cox proportional hazards models were fit separately for time from biopsy to complete remission and time to the composite of end-stage kidney disease and a 40% decline in eGFR to assess the association of molecular cluster and TNF score with clinical outcomes. Models were adjusted for diagnosis (MCD, FSGS), eGFR, and UPCR. IF and glomerular sclerosis were assumed to be on the causal pathway and therefore not included in the models. Pearson's correlation was used to assess the relationship between the TNF score, marker tissue mRNA expression, and urinary concentration. Linear regression models were fit to assess the association of urinary markers and clinical features with TNF score, and to calculate a predicted score. Pearson's correlation was used to correlate the predicted and observed TNF activation scores. Analyses were performed using STATA, v12.1. Nuclei were prepared from the biopsy tissue of 10 NEPTUNE participants (5 from cluster 3, with high TNF activity scores [defined as TNF High], and 5 from clusters 1 and 2, with lower TNF activity scores [defined as TNF Low]) stored in RNAlater using protocols from the Kidney Precision Medicine Project27Lake B.B. Chen S. Hoshi M. et al.A single-nucleus RNA-sequencing pipeline to decipher the molecular anatomy and pathophysiology of human kidneys.Nat Commun. 2019; 10: 2832Crossref PubMed Scopus (140) Google Scholar,28Lake B.B. Menon R. Winfree S. et al.An atlas of healthy and injured cell states and niches in the human kidney.bioRxiv. 2021; (2021.2007.2028.454201)Google Scholar (Supplementary Methods, GSE213030). Nuclear cluster annotation was determined by defining enriched genes in each cell cluster and comparing cluster selective gene profiles with previously identified human kidney cell marker gene sets.26Menon R. Otto E.A. Hoover P. et al.Single cell transcriptomics identifies focal segmental glomerulosclerosis remission endothelial biomarker.JCI Insight. 2020; 5e133267Crossref Scopus (77) Google Scholar, 27Lake B.B. Chen S. Hoshi M. et al.A single-nucleus RNA-sequencing pipeline to decipher the molecular anatomy and pathophysiology of human kidneys.Nat Commun. 2019; 10: 2832Crossref PubMed Scopus (140) Google Scholar, 28Lake B.B. Menon R. Winfree S. et al.An atlas of healthy and injured cell states and niches in the human kidney.bioRxiv. 2021; (2021.2007.2028.454201)Google Scholar Kidney organoids were generated from UM77-2 human embryonic stem cells as previously described.29Harder J.L. Menon R. Otto E.A. et al.Organoid single cell profiling identifies a transcriptional signature of glomerular disease.JCI Insight. 2019; 4e122697Crossref PubMed Scopus (55) Google Scholar Organoids were treated with TNF (R&D Systems, Cat# 10291-TA) resuspended in phosphate-buffered saline on day 23 at the indicated concentrations. Organoid supernatants were removed at specified times, RNA extracted, and sequenced (Supplementary Methods). Organoid culture supernatants and cell lysates were diluted 150-fold to measure MCP-1 and 10-fold to measure TIMP-1 using the enzyme-linked immunosorbent assay as described above. Quantitative real-time polymerase chain reaction analysis was performed in triplicate using TaqMan Fast Universal PCR Master Mix (2X) for CCL2, TIMP-1, and glyceraldehyde-3-phosphate dehydrogenase. Transcriptomic profiles of microdissected kidney biopsy compartments were used to group participants into distinct subclusters. Because of the known association of tubulointerstitial changes with risk of loss of eGFR, tubulointerstitial transcriptional data were analyzed first. Transcriptomes from NEPTUNE participants clustered into 3 groups (n = 85, 76, and 59, respectively), with 1 cluster (T3) demonstrating the highest cluster stability (Figure 2a). The delta-K revealed that the 3-cluster solution was optimal across clustering approaches (Supplementary Figure S1A–C). To validate the molecular profiles identified in NEPTUNE, Kmeans, PAM, and hierarchical consensus clustering22Wilkerson M.D. Hayes D.N. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking.Bioinformatics. 2010; 26: 1572-1573Crossref PubMed Scopus (2603) Google Scholar,23Monti S. Tamayo P. Mesirov J.P. Golub T. Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data.Mach Learn. 2004; 52: 91-118Crossref Scopus (1424) Google Scholar were also applied to the tubulointerstitial transcriptome data from 2 independent FSGS/MCD cohorts, ERCB (N = 30) and H3Africa (N = 35), resulting in 3 distinct clusters (Figure 2b and c) with high cluster stability. Glomerular compartment clustering also identified 3 clusters (Supplementary Figure S2A and B), with a transcriptional signature largely shared with the tubulointerstitium (Figure 2d). Differential expression analysis of the tubulointerstitial transcripts between T3 and the other 2 clusters in each cohort showed a robust, directionally conserved molecular signal across cohorts (correlation of fold change 0.94, P < 0.001, for NEPTUNE vs. ERCB and 0.93, P < 0.001, for NEPTUNE vs. H3; Figure 2e and f). Finally, of the 179 NEPTUNE participants with measured IF, clustering was repeated in only those participants with IF <25% (n = 148). All 26 participants, originally in T3 from this group, again clustered together. NEPTUNE participants in T3 were older and had a lower eGFR, greater IF, and higher UPCR at biopsy (Table 1; Supplementary Figure S3). In ERCB and H3Africa, participants in T3 also had a lower eGFR and were older. Although T3 had a greater proportion of FSGS in all 3 cohorts, it also included participants with MCD (Figure 2g, Supplementary Figure S1D and E). In an unadjusted survival model, NEPTUNE T3 participants were more likely to reach the composite of end-stage kidney disease or a 40% decline in eGFR (unadjusted hazard ratio [HR]: 5.23 [95% confidence interval: 1.9, 14.5], P < 0.001, for overall differences in curves; Figure 2h) and fewer complete proteinuria remission events were observed (unadjusted HR: 0.73 [95% confidence interval: 0.43, 1.26], P = 0.068, for overall difference in curves; Figure 2i) compared with T1.Table 1Clinical characteristics of participants summarized by cluster identity and cohortNEPTUNEAll (N = 220)Cluster 1 (N = 85)Cluster 2 (N = 76)Cluster 3 (N = 59)P valueAge, mean (SD)28.2 (21.7)22.3 (19.5)26.8 (22.6)38.5 (20.0)<0.0001Female, n (%)89 (40)33 (39)30 (39)26 (44)0.82Adult, n (%)112 (59)31 (36)35 (46)46 (78)<0.0001eGFR, mean (SD)89.5 (46.8)102.1 (40.7)104.2 (48.9)52.3 (29.2)<0.0001Albumin, mean (SD)3.62 (0.89)3.68 (0.86)3.62 (0.89)3.54 (0.95)0.3916FSGS, n (%)114 (52)32 (38)37 (49)45 (76)<0.0001UPCR, median (IQR)2.76 (1.0, 7.04)1.9 (0.6, 7.1)2.56 (1.02, 5.5)5.35 (2.0, 10.6)<0.0001% IF, median (IQR)4 (0, 16)1.5 (0,5)3 (0, 8)20 (10, 55)<0.0001Disease duration before biopsy, mo, mean (SD)29.47 (69.46)26.78 (46.56)18 (50.6)49 (108)0.30On RAAS blockade, n (%)86 (39)34 (40)26 (34)26 (44)0.0005On IST, n (%)101 (46)49 (58)37 (49)15 (25)0.0005ERCBCluster 1 (N = 18)Cluster 2 (N = 4)Cluster 3 (N = 8)P valueAge, mean (SD)42.2 (19.0)29.4 (6.6)49.2 (18.3)0.63Female, n (%)10 (56)1 (25)3 (38)0.59eGFR, mean (SD)92.2 (34.5)119.0 (4.0)43.2 (31.0)0.02FSGS, n (%)8 (44)2 (50)7 (87)0.135H3AfricaCluster 1 (N = 14)Cluster 2 (N = 16)Cluster 3 (N = 5)P valueAge, mean (SD)24.1 (10.2)30.0 (14.1)30.8 (10.7)0.19Female, n (%)3 (21)4 (25)0 (0)0.90eGFR, mean (SD)89.8 (42.6)109.8 (14.7)25.8 (11.7)0.03FSGS, n (%)7 (50)4 (25)4 (80)0.07UPCR, median (IQR)1.4 (1.1, 2.2)1.1 (0.3, 2.8)3.6 (1.8, 7.5)0.05% IF, median (IQR)0 (0, 9)0 (0, 1)40 (0, 40)0.01Disease duration less than 6 mo, n (%)2 (14)4 (25)4 (80)0.02IST in the past 6 mo, n (%)5 (36)4 (25)1 (20)0.71eGFR, estimated glomerular filtration rate (ml/min per 1.73 m2); ERCB, European Renal cDNA Bank; FSGS, focal segmental glomerulosclerosis; H3Africa, Human Heredity and Health in Africa Kidney Disease Research Network cohort; IF, interstitial fibrosis; IQR, interquartile range; IST, immunosuppressive therapy; NEPTUNE, North American Nephrotic Syndrome Study Network; RAAS, renin angiotensin aldosterone system; UPCR, urine protein-to-creatinine ratio (mg/mg). Open table in a new tab eGFR, estimated glomerular filtration rate (ml/min per 1.73 m2); ERCB, European Renal cDNA Bank; FSGS, focal segmental glomerulosclerosis; H3Africa, Human Heredity and Health in Africa Kidney Disease Research Network cohort; IF, interstitial fibrosis; IQR, interquartile range; IST, immunosuppressive therapy; NEPTUNE, North American Nephrotic Syndrome Study Network; RAAS, renin angiotensin aldosterone system; UPCR, urine protein-to-creatinine ratio (mg/mg). Differential mRNA expression profiles were used to elucidate the molecular functions associated with cluster T3. In NEPTUNE, there were 2721 transcripts in T3 with an absolute 1.5-fold change and q < 0.05 (2199 upregulated, 522 downregulated) compared with T1 and T2 (Supplementary Table S1A). This gene set was analyzed to identify enriched canonical pathways, predicted upstream regulators,30Krämer A. Green J. Pollard Jr., J. Tugendreich S. Causal analysis approaches in Ingenuity Pathway Analysis.Bioinformatics. 2014; 30: 523-530Crossref PubMed Scopus (3268) Google Scholar and gene interaction networks. These analyses converged on TNF pathway activation. In signal transduction pathway over-representation (enrichment), the granulocyte adhesion and diapedesis signal transduction pathway had the highest enrichment score (−log(p) = 21.4). Of the 180 genes in this pathway, 70 (38.9%) were found differentially regulated in T3, including TNF (2.4-fold upregulated in T3, q < 0.001, Figure 3a26Menon R. Otto E.A. Hoover P. et al.Single cell transcriptomics identifies focal segmental glomerulosclerosis remission endothelial biomarker.JCI Insight. 2020; 5e133267Crossref Scopus (77) Google Scholar). Causal analysis of predicted upstream regulators predicted TNF as the top biological mediator activated in T3 (IPA network Z-score = 13.2, enrichment P = 2.09E–120). An expanded causal mechanistic network centered on downstream effects of predicted TNF activation (Figure 3b) explained 48% (1299 of 2721) of the differentially expressed genes between T3 and T1 and 2. Regulated transcripts included multiple transcription factors previously implicated in chronic kidney disease progression, such as nuclear factor kappa B (including NFκB 1 (p105/p50), RELA (p65) subunits)11Schmid H. Boucherot A. Yasuda Y. et al.Modular activation of nuclear factor-kappaB transcriptional prog